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今天的主题是：Syntriever: How to Train Your Retriever with Synthetic Data from LLMs

Summary

The provided research paper introduces Syntriever, a novel framework for training information retrieval systems by leveraging synthetic data generated from large language models (LLMs). This approach consists of two key stages: distillation, where relevant and irrelevant passages are synthesized using LLMs and used to train the retriever, and alignment, where the retriever's output is fine-tuned based on preferences expressed by LLMs for pairs of retrieved passages. Syntriever addresses the challenge of distilling knowledge from black-box LLMs, achieving state-of-the-art results on various information retrieval benchmarks by effectively combining synthetic data generation with preference-based learning. The framework demonstrates that even smaller retrieval models can significantly improve their performance by learning from the knowledge and ranking abilities of LLMs through this synthetic data and alignment process.

本文提出了 Syntriever，一个创新框架，用于通过大语言模型（LLMs）生成的合成数据来训练信息检索系统。该方法包括两个关键阶段：蒸馏（distillation），即使用 LLMs 合成相关和不相关的段落，并用于训练检索器；以及对齐（alignment），即根据 LLMs 对检索段落对的偏好来微调检索器的输出。Syntriever 解决了从黑箱 LLMs中提取知识的挑战，通过有效结合合成数据生成与基于偏好的学习，在多个信息检索基准上取得了最先进的成果。该框架表明，甚至较小的检索模型也可以通过这种合成数据和对齐过程，从 LLMs 的知识和排序能力中学习，从而显著提升其性能。

原文链接：https://arxiv.org/abs/2502.03824